Advanced Lane Finding Project

The goals / steps of this project are the following:

  • Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
  • Apply a distortion correction to raw images.
  • Use color transforms, gradients, etc., to create a thresholded binary image.
  • Apply a perspective transform to rectify binary image ("birds-eye view").
  • Detect lane pixels and fit to find the lane boundary.
  • Determine the curvature of the lane and vehicle position with respect to center.
  • Warp the detected lane boundaries back onto the original image.
  • Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.

First, I'll compute the camera calibration using chessboard images

In [1]:
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pickle

from ipywidgets import interact, interactive, fixed

%matplotlib inline

# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1,2)

# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.

# Make a list of calibration images
images = glob.glob('./camera_cal/calibration*.jpg')

fig, axes = plt.subplots(5,4, figsize=(30, 30))
axes = axes.ravel()

# Step through the list and search for chessboard corners
for i,fname in enumerate(images):
    img = cv2.imread(fname)
    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

    # Find the chessboard corners
    ret, corners = cv2.findChessboardCorners(gray, (9,6),None)

    # If found, add object points, image points
    if ret == True:
        objpoints.append(objp)
        imgpoints.append(corners)

        # Draw and display the corners
        img = cv2.drawChessboardCorners(img, (9,6), corners, ret)
        axes[i].axis('off')
        axes[i].imshow(img)

Camera Calibration and Saving up the required parameters

In [2]:
img = cv2.imread('./camera_cal/calibration10.jpg')
#img_size = (img.shape[1], img.shape[0])

#Camera calibration, given object points, image points, and the shape of the grayscale image:

ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)

undist = cv2.undistort(img, mtx, dist, None, mtx)

# Saving the camera calibration parameters
dist_pickle = {}
dist_pickle["mtx"] = mtx
dist_pickle["dist"] = dist
dist_pickle["rvecs"] = rvecs
dist_pickle["tvecs"] = tvecs
pickle.dump( dist_pickle, open( "CameraCalibration.p", "wb" ) )

# Making a method for the pipeline that will be made in later section
def undistort(img):
    undist_img = cv2.undistort(img, mtx, dist, None, mtx)
    return undist_img

Undistorting images using above camera calibration

In [4]:
# list of test images
images = glob.glob('test_images/test*.jpg')

f, axarr = plt.subplots(6, 2, figsize=(20, 40))
undist_images = []

#Undistorting a test image:
for i, fname in enumerate(images):
    img = mpimg.imread(fname,0) 
    undist = cv2.undistort(img, mtx, dist, None, mtx) 
    undist_images.append(undist)
    axarr[i,0].axis('off')
    axarr[i,0].set_title('Original Image', fontsize=15)
    axarr[i,0].imshow(img)
    axarr[i,1].axis('off')
    axarr[i,1].set_title('Undistorted Image', fontsize=15)
    axarr[i,1].imshow(undist)

Defining color space thresholding function

In [5]:
# HLS Color space
def hls_color_thresh(img, threshLow, threshHigh):
    imgHLS = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)

    binary_output = np.zeros((img.shape[0], img.shape[1]))
    binary_output[(imgHLS[:,:,0] >= threshLow[0]) & (imgHLS[:,:,0] <= threshHigh[0]) & (imgHLS[:,:,1] >= threshLow[1])  & (imgHLS[:,:,1] <= threshHigh[1])  & (imgHLS[:,:,2] >= threshLow[2]) & (imgHLS[:,:,2] <= threshHigh[2])] = 1
    return binary_output

Applying Sobel transform and thresholding to undistorted image

In [6]:
#Sobel in x direction & Magnitude threshold
def sobel_x(img, sobel_kernel=3,min_thres = 20, max_thres =100):
    # Apply the following steps to img
    # 1) Convert to grayscale
    imghsl = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
    # 2) Take the gradient in x and y separately
    #Channels L and S from HLS
    sobelx1 = cv2.Sobel(imghsl[:,:,1], cv2.CV_64F, 1,0, ksize=sobel_kernel)
    sobelx2 = cv2.Sobel(imghsl[:,:,2], cv2.CV_64F, 1,0, ksize=sobel_kernel)
        
    # 4) Scale to 8-bit (0 - 255) and convert to type = np.uint8
    scaled_sobelx1 = np.uint8(255*sobelx1/ np.max(sobelx1))
    scaled_sobelx2 = np.uint8(255*sobelx2/ np.max(sobelx2))

    # 5) Create a binary mask where mag thresholds are met
    binary_outputx1 = np.zeros_like(scaled_sobelx1)
    binary_outputx1[(scaled_sobelx1 >= min_thres) & (scaled_sobelx1 <= max_thres)] = 1

    binary_outputx2 = np.zeros_like(scaled_sobelx2)
    binary_outputx2[(scaled_sobelx2 >= min_thres) & (scaled_sobelx2 <= max_thres)] = 1

    binary_output = np.zeros_like(scaled_sobelx1)
    binary_output[(binary_outputx1 ==1) | (binary_outputx2 ==1)]=1
    # 6) Return this mask as your binary_output image
    return binary_output


def mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255)):    
    # Apply the following steps to img
    # 1) Convert to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    # 2) Take the gradient in x and y separately
    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1,0, ksize=sobel_kernel)
    sobely = cv2.Sobel(gray, cv2.CV_64F, 0,1, ksize=sobel_kernel)
    
    # 3) Calculate the magnitude 
    gradmag = np.sqrt(sobelx**2 + sobely**2)
    
    # 4) Scale to 8-bit (0 - 255) and convert to type = np.uint8
    scaled_sobel = np.uint8(255*gradmag / np.max(gradmag))
       
    # 5) Create a binary mask where mag thresholds are met
    binary_output = np.zeros_like(scaled_sobel)
    binary_output[(scaled_sobel >= mag_thresh[0]) & (scaled_sobel <= mag_thresh[1])] = 1


    # 6) Return this mask as your binary_output image
    return binary_output


#Direction threshold
def dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
    
    # Apply the following steps to img
    # 1) Convert to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

    # 2) Take the gradient in x and y separately
    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1,0, ksize=sobel_kernel)
    sobely = cv2.Sobel(gray, cv2.CV_64F, 0,1, ksize=sobel_kernel)

    # 3) Take the absolute value of the x and y gradients
    abs_sobelx = np.absolute(sobelx)
    abs_sobely = np.absolute(sobely)

    # 4) Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient 
    absgraddir = np.arctan2(abs_sobely, abs_sobelx) 

    # 5) Create a binary mask where direction thresholds are met
    binary_output = np.zeros_like(absgraddir)
    binary_output[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1

    # 6) Return this mask as your binary_output image
    return binary_output

#Both Magnitude and direction threshold
def mag_dir_thresh(img, sobel_kernel=3, mag_thresh=(0, 255), dir_thresh=(0,np.pi/2)):
    # Apply the following steps to img
    # 1) Convert to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    
    # 2) Take the gradient in x and y separately
    sobelx = cv2.Sobel(img, cv2.CV_64F, 1,0, ksize=sobel_kernel) 
    sobely = cv2.Sobel(img, cv2.CV_64F, 0,1, ksize=sobel_kernel)
    
    # 3) Calculate the magnitude 
    gradmag = np.sqrt(sobelx**2 + sobely**2)
    
    #Calc angle
    abs_sobelx = np.absolute(sobelx)
    abs_sobely = np.absolute(sobely)
    absgraddir = np.arctan2(abs_sobely, abs_sobelx) 

    # 4) Scale to 8-bit (0 - 255) and convert to type = np.uint8
    scaled_sobel = np.uint8(255*gradmag / np.max(gradmag))
       
    # 5) Create a binary mask where mag thresholds are met
    binary_output = np.zeros_like(scaled_sobel)
    binary_output[(scaled_sobel >= mag_thresh[0]) & (scaled_sobel <= mag_thresh[1]) & (absgraddir >= dir_thresh[0]) & (absgraddir <= dir_thresh[1]) ] = 1


    # 6) Return this mask as binary_output image
    return binary_output

Examples of gradient and color thresholding

In [9]:
#Examples of magnitude and direction thresholds
plt.figure(figsize=(10,8))

img = cv2.imread("test_images/test1.jpg")
imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

magThr =mag_thresh(imgRGB, 3, (50, 100))

dirThr =dir_threshold(imgRGB, 9,(np.pi/240/90, np.pi/2*60/90))

#Sobel x only
imgThr = sobel_x(imgRGB,9,80,220) #Sobel x

print("Examples of magnitude and direction thresholds")
plt.figure(figsize=(40,20))

plt.subplot(5,1,1)
plt.title('Original Image')
fig =plt.imshow(imgRGB)

plt.subplot(5,1,2)
plt.title('Magnitude threshold Image')
fig =plt.imshow(magThr,cmap = 'gray')

plt.subplot(5,1,3)
plt.title('Direction threshold Image')
fig =plt.imshow(dirThr,cmap = 'gray')

plt.subplot(5,1,4)
plt.title('Sobel x only threshold Image')
fig =plt.imshow(imgThr,cmap = 'gray')

# on test images

img = cv2.imread("test_images/test1.jpg")
imgHLS = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)

plt.figure(figsize=(10,8))

plt.subplot(3,1,1)
plt.title('Test image H')
fig =plt.imshow(imgHLS[:,:,0],cmap='gray')
plt.subplot(3,1,2)
plt.title('Test Image L')
fig =plt.imshow(imgHLS[:,:,1],cmap='gray')
plt.subplot(3,1,3)
plt.title('Test image S')
fig =plt.imshow(imgHLS[:,:,2],cmap='gray')
Examples of magnitude and direction thresholds
<matplotlib.figure.Figure at 0x7fa4e412f5c0>

Perspective transform and warped image

In [11]:
#Perspective transfomation

src = np.float32([[585, 450], [204, 720], [1126, 720], [695, 450]])
dst = np.float32([[320, 0], [320, 720], [960,720], [960, 0]])

M_persp = cv2.getPerspectiveTransform(src, dst)
Minv_persp = cv2.getPerspectiveTransform(dst, src)

img_size = (imgThr.shape[1], imgThr.shape[0])
binary_warped = cv2.warpPerspective(imgThr, M_persp, img_size, flags=cv2.INTER_LINEAR)


plt.figure(figsize=(30,20))

plt.subplot(4,1,1)
plt.title('Binary image')
fig =plt.imshow(imgThr, cmap='gray')

plt.subplot(4,1,2)
plt.title('Binary perspective')
fig =plt.imshow(binary_warped, cmap='gray')

# Warp image

def warp(img, src, dst):
    h,w = img.shape[:2]
    # use cv2.getPerspectiveTransform() to get M, the transform matrix, and Minv, the inverse
    M = cv2.getPerspectiveTransform(src, dst)
    Minv = cv2.getPerspectiveTransform(dst, src)
    # use cv2.warpPerspective() to warp your image to a top-down view
    warped = cv2.warpPerspective(img, M, (w,h), flags=cv2.INTER_LINEAR)
    return warped, M, Minv
    

h,w = undist_images[5].shape[:2]

# # define source and destination points for transform for test3.jpg
src = np.float32([(575,475),
                  (740,475), 
                  (260,685), 
                  (1075,685)])

dst = np.float32([(450,0),
                  (w-450,0),
                  (450,h),
                  (w-450,h)])

undist_images_warp, M, Minv = warp(undist_images[5], src, dst)

# Visualize warp
f, (axes1, axes2) = plt.subplots(1, 2, figsize=(20,10))
f.subplots_adjust(hspace = .2, wspace=.05)
axes1.imshow(undist_images[5])

x = [src[0][0],src[2][0],src[3][0],src[1][0],src[0][0]]
y = [src[0][1],src[2][1],src[3][1],src[1][1],src[0][1]]

axes1.plot(x,y, color='#44ffff', alpha=0.5, linewidth=4, solid_capstyle='round', zorder=2)
axes1.imshow(undist_images[5])
axes1.set_ylim([h,0])
axes1.set_xlim([0,w])
axes1.set_title('Undistorted Image', fontsize=20)

axes2.imshow(undist_images_warp)
axes2.set_title('warped Image', fontsize=20)
Out[11]:
<matplotlib.text.Text at 0x7fa4dd2b21d0>

Fit the lines found onto a polynomial

In [14]:
def fitlines(binary_warped):
    # Assuming you have created a warped binary image called "binary_warped"
    # Take a histogram of the bottom half of the image
    histogram = np.sum(binary_warped[np.int(binary_warped.shape[0]/2):,:], axis=0)
    # Create an output image to draw on and  visualize the result
    out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
    
   
    # Find the peak of the left and right halves of the histogram
    # These will be the starting point for the left and right lines
    midpoint = np.int(histogram.shape[0]/2)
    leftx_base = np.argmax(histogram[:midpoint])
    rightx_base = np.argmax(histogram[midpoint:]) + midpoint

    # Choose the number of sliding windows
    nwindows = 9
    # Set height of windows
    window_height = np.int(binary_warped.shape[0]/nwindows)
    # Identify the x and y positions of all nonzero pixels in the image
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    # Current positions to be updated for each window
    leftx_current = leftx_base
    rightx_current = rightx_base
    # Set the width of the windows +/- margin
    margin = 100
    # Set minimum number of pixels found to recenter window
    minpix = 50
    # Create empty lists to receive left and right lane pixel indices
    left_lane_inds = []
    right_lane_inds = []

    # Step through the windows one by one
    for window in range(nwindows):
        # Identify window boundaries in x and y (and right and left)
        win_y_low = binary_warped.shape[0] - (window+1)*window_height
        win_y_high = binary_warped.shape[0] - window*window_height
        win_xleft_low = leftx_current - margin
        win_xleft_high = leftx_current + margin
        win_xright_low = rightx_current - margin
        win_xright_high = rightx_current + margin
        # Draw the windows on the visualization image
        cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2) 
        cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2) 
        # Identify the nonzero pixels in x and y within the window
        good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
        good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
        # Append these indices to the lists
        left_lane_inds.append(good_left_inds)
        right_lane_inds.append(good_right_inds)
        # If you found > minpix pixels, recenter next window on their mean position
        if len(good_left_inds) > minpix:
            leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
        if len(good_right_inds) > minpix:        
            rightx_current = np.int(np.mean(nonzerox[good_right_inds]))

    # Concatenate the arrays of indices
    left_lane_inds = np.concatenate(left_lane_inds)
    right_lane_inds = np.concatenate(right_lane_inds)

    # Extract left and right line pixel positions
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds] 
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds] 
    
    
    # Fit a second order polynomial to each
    if len(leftx) == 0:
        left_fit =[]
    else:
        left_fit = np.polyfit(lefty, leftx, 2)
    
    if len(rightx) == 0:
        right_fit =[]
    else:
        right_fit = np.polyfit(righty, rightx, 2)
    

    
    out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
    out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]


    return left_fit, right_fit,out_img
In [15]:
#Visualization of lines fitted
img = cv2.imread("test_images/test1.jpg")
#img = cv2.imread("test_images/straight_lines2.jpg")
imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

img_undist = cv2.undistort(imgRGB, mtx, dist, None, mtx)

#2.Magnitude Threshold
#Threshold color    
yellow_low = np.array([0,100,100])
yellow_high = np.array([50,255,255])
white_low = np.array([18,0,180])
white_high = np.array([255,80,255])
global ref_left 
global ref_right
global left_fit
global right_fit

imgThres_yellow = hls_color_thresh(img_undist,yellow_low,yellow_high)
imgThres_white = hls_color_thresh(img_undist,white_low,white_high)
imgThr_sobelx = sobel_x(img_undist,9,80,220) #Sobel x only

img_mag_thr =np.zeros_like(imgThres_yellow)
img_mag_thr[(imgThres_yellow==1) | (imgThres_white==1) | (imgThr_sobelx==1)] =1
img_mag_thr[(imgThres_yellow==1) | (imgThres_white==1)] =1


#3. Birds-eye
#Perspective array from before
img_size = (img_mag_thr.shape[1], img_mag_thr.shape[0])
binary_warped = cv2.warpPerspective(img_mag_thr, M_persp, img_size, flags=cv2.INTER_LINEAR)

left_fit, right_fit,out_img = fitlines(binary_warped)


print(out_img.shape)
print(np.max(out_img))


ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
    
    

plt.figure(figsize=(30,20))
plt.subplot(3,1,1)
plt.imshow(binary_warped, cmap='gray')

plt.subplot(3,1,2)

plt.imshow(out_img)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)

plt.subplot(3,1,2)
binary_warped2 = np.zeros((720, 1280,3))
binary_warped2[:,:,0] = binary_warped
binary_warped2[:,:,1] = binary_warped
binary_warped2[:,:,2] = binary_warped
plt.imshow(out_img)
result = cv2.addWeighted(binary_warped2, .8, out_img, .8, 0)
plt.imshow(result)
(720, 1280, 3)
255.0
Out[15]:
<matplotlib.image.AxesImage at 0x7fa4dd4b3e48>
In [16]:
def fit_continuous(left_fit, right_fit, binary_warped):
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    margin = 100
    left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] + margin))) 
    right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] + margin)))  

    # Again, extract left and right line pixel positions
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds] 
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]
    
    # Fit a second order polynomial to each
    if len(leftx) == 0:
        left_fit_updated =[]
    else:
        left_fit_updated = np.polyfit(lefty, leftx, 2)
    
    
    if len(rightx) == 0:
        right_fit_updated =[]
    else:
        right_fit_updated = np.polyfit(righty, rightx, 2)
        
    return  left_fit_updated, right_fit_updated

Calculate curvature

In [17]:
def curvature(left_fit, right_fit, binary_warped):
    ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
    y_eval = np.max(ploty)
    
    # from estimate of US lane regulations (approx)
    ym_per_pix = 30/720 # meters per pixel in y dimension
    xm_per_pix = 3.7/700 # meters per pixel in x dimension

    
    # Calculate the new radii of curvature
    left_curverad = ((1 + (2*left_fit[0]*y_eval*ym_per_pix + left_fit[1])**2)**1.5) / np.absolute(2*left_fit[0])
    right_curverad = ((1 + (2*right_fit[0]*y_eval*ym_per_pix + right_fit[1])**2)**1.5) / np.absolute(2*right_fit[0])
    center = (((left_fit[0]*720**2+left_fit[1]*720+left_fit[2]) +(right_fit[0]*720**2+right_fit[1]*720+right_fit[2]) ) /2 - 640)*xm_per_pix
    
    # Now our radius of curvature is in meters
    print(left_curverad, 'm', right_curverad, 'm')
    return left_curverad, right_curverad, center

Draw fitted lane on image

In [18]:
def drawLine(undist, warped,left_fit, right_fit):
    # Create an image to draw the lines on
    warp_zero = np.zeros_like(warped).astype(np.uint8)
    color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
    
    ploty = np.linspace(0, warped.shape[0]-1, warped.shape[0] )
    # Fit new polynomials to x,y in world space
    left_fitx = left_fit[0]*ploty**2+left_fit[1]*ploty+left_fit[2]
    right_fitx = right_fit[0]*ploty**2+right_fit[1]*ploty+right_fit[2] 
    
    #print(left_fitx)
    # Recast the x and y points into usable format for cv2.fillPoly()
    pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
    pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
    pts = np.hstack((pts_left, pts_right))

    # Draw the lane onto the warped blank image
    cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))

    
    # Warp the blank back to original image space using inverse perspective matrix (Minv)
    newwarp = cv2.warpPerspective(color_warp, Minv_persp, (color_warp.shape[1], color_warp.shape[0])) 

    # Combine the result with the original image
    
    result = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)

    return(result, color_warp)

Pipeline for image

In [19]:
global counter
counter=0
ref_left =np.array([-0.0001,0,400])
ref_right=np.array([-0.0001,0,1000])   
left_fit =np.array([-0.0001,0,400])
right_fit=np.array([-0.0001,0,1000])   

def process_image(image):
    #1. Camera correction
    #Calibration arrays pre-calculated
    img_undist = cv2.undistort(image, mtx, dist, None, mtx)
    global counter
    
    #2.Magnitude Threshold
    #Threshold color    
    yellow_low = np.array([0,100,100])
    yellow_high = np.array([50,255,255])
    white_low = np.array([18,0,180])
    white_high = np.array([255,80,255])
    global ref_left 
    global ref_right
    global left_fit
    global right_fit

    imgThres_yellow = hls_color_thresh(img_undist,yellow_low,yellow_high)
    imgThres_white = hls_color_thresh(img_undist,white_low,white_high)
    imgThr_sobelx = sobel_x(img_undist,9,80,220) #Sobel x

    img_mag_thr =np.zeros_like(imgThres_yellow)
    #imgThresColor[(imgThres_yellow==1) | (imgThres_white==1)] =1
    img_mag_thr[(imgThres_yellow==1) | (imgThres_white==1) | (imgThr_sobelx==1)] =1
        
    #3. Birds-eye
    #Perspective array pre-calculated
    img_size = (img_mag_thr.shape[1], img_mag_thr.shape[0])
    binary_warped = cv2.warpPerspective(img_mag_thr, M_persp, img_size, flags=cv2.INTER_LINEAR)
    
    #4. Detect lanes and return fit curves
    
    if counter==0:
        left_fit, right_fit,out_imgfit = fitlines(binary_warped)
    else:
        left_fit, right_fit = fit_continuous(left_fit, right_fit, binary_warped)
    
    status_sanity, d0, d1 =sanity_check(left_fit, right_fit, 0, .55)  
    #Calc curvature and center
    if status_sanity  == True:        
        #Save as last reliable fit
        ref_left, ref_right = left_fit, right_fit        
        counter+=1
    else:        #Use the last realible fit
        left_fit, right_fit = ref_left, ref_right
        
    left_curv, right_curv, center_off = curvature(left_fit, right_fit, binary_warped)

    #Warp back to original and merge with image    
    img_out, img_birds = drawLine(img_undist, binary_warped,left_fit, right_fit)

    
    #Write curvature and center in image
    TextL = "Left curv: " + str(int(left_curv)) + " m"
    TextR = "Right curv: " + str(int(right_curv))+ " m"
    TextC = "Center offset: " + str(round( center_off,2)) + "m"

    fontScale=1
    thickness=2
    
    fontFace = cv2.FONT_HERSHEY_SIMPLEX


    cv2.putText(img_out, TextL, (130,40), fontFace, fontScale,(200,255,155), thickness,  lineType = cv2.LINE_AA)
    cv2.putText(img_out, TextR, (130,70), fontFace, fontScale,(200,255,155), thickness,  lineType = cv2.LINE_AA)
    cv2.putText(img_out, TextC, (130,100), fontFace, fontScale,(200,255,155), thickness,  lineType = cv2.LINE_AA)
   
    return img_out

Sanity check as instructed in rubric

In [23]:
def sanity_check(left_fit, right_fit, minSlope, maxSlope):
    #Performs a sanity check on the lanes
    #Check 1: check if left and right fits exists
    #Check 2: Calculates the tangent between left and right in two points, and check if it is in a reasonable threshold
    xm_per_pix = 3.7/700 # meters per pixel in x dimension
    if len(left_fit) ==0 or len(right_fit) == 0:
        status = False
        d0=0
        d1=0
        #Previous fitlines routine returns empty list to them if not finds
    else:
        #Difference of slope
        L_0 = 2*left_fit[0]*460+left_fit[1]
        R_0 = 2*right_fit[0]*460+right_fit[1]
        d0 =  np.abs(L_0-R_0)

        L_1 = 2*left_fit[0]*720+left_fit[1]
        R_1 = 2*right_fit[0]*720+right_fit[1]
        d1 =  np.abs(L_1-R_1)

        
        if d0>= minSlope and d0<= maxSlope and d1>= minSlope and d1<= maxSlope:
            status = True
        else:
            status = False
            
    return(status, d0, d1)
In [24]:
img = cv2.imread("test_images/test1.jpg")
#img = cv2.imread("test_images/straight_lines1.jpg")
imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

img2= process_image(imgRGB)

plt.figure(figsize=(10,15))
#plt.figure(figsize=(5,10))

  
plt.figure(figsize=(30,20))
plt.imshow(img2)
1201.763849 m 2080.02000126 m
Out[24]:
<matplotlib.image.AxesImage at 0x7fa4dd305160>
<matplotlib.figure.Figure at 0x7fa4e4197c50>

Video setup

In [25]:
from moviepy.editor import VideoFileClip
from IPython.display import HTML
import moviepy as mve
Imageio: 'ffmpeg.linux64' was not found on your computer; downloading it now.
Try 1. Download from https://github.com/imageio/imageio-binaries/raw/master/ffmpeg/ffmpeg.linux64 (27.2 MB)
Downloading: 8192/28549024 bytes (0.0360448/28549024 bytes (1.31163264/28549024 bytes (4.1%1933312/28549024 bytes (6.8%2924544/28549024 bytes (10.23538944/28549024 bytes (12.44440064/28549024 bytes (15.65226496/28549024 bytes (18.36242304/28549024 bytes (21.97061504/28549024 bytes (24.77634944/28549024 bytes (26.78388608/28549024 bytes (29.49216000/28549024 bytes (32.39805824/28549024 bytes (34.310633216/28549024 bytes (37.2%11362304/28549024 bytes (39.8%11837440/28549024 bytes (41.5%12427264/28549024 bytes (43.5%13230080/28549024 bytes (46.3%13967360/28549024 bytes (48.9%14655488/28549024 bytes (51.3%15507456/28549024 bytes (54.3%16343040/28549024 bytes (57.2%16982016/28549024 bytes (59.5%17752064/28549024 bytes (62.2%18096128/28549024 bytes (63.4%18595840/28549024 bytes (65.1%19529728/28549024 bytes (68.4%20144128/28549024 bytes (70.6%20897792/28549024 bytes (73.2%21438464/28549024 bytes (75.1%22134784/28549024 bytes (77.5%22790144/28549024 bytes (79.8%23289856/28549024 bytes (81.6%24174592/28549024 bytes (84.7%24780800/28549024 bytes (86.8%25370624/28549024 bytes (88.9%26124288/28549024 bytes (91.5%26845184/28549024 bytes (94.0%27664384/28549024 bytes (96.9%28450816/28549024 bytes (99.7%28549024/28549024 bytes (100.0%)
  Done
File saved as /root/.imageio/ffmpeg/ffmpeg.linux64.
In [26]:
#Create video file pipeline
counter=0
output = 'out_test_video.mp4'
clip1 = VideoFileClip("project_video.mp4")
out_clip = clip1.fl_image(process_image)
%time out_clip.write_videofile(output, audio=False)
print(counter)
1581.63670627 m 3163.43672394 m
[MoviePy] >>>> Building video out_test_video.mp4
[MoviePy] Writing video out_test_video.mp4
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[MoviePy] Done.
[MoviePy] >>>> Video ready: out_test_video.mp4 

CPU times: user 4min 37s, sys: 8.28 s, total: 4min 45s
Wall time: 4min 40s
1261
In [27]:
HTML("""
<video  width="960" height="540" controls>
  <source src="{0}">
</video>
""".format(output))
Out[27]: